We follow the standard evaluation setup proposed by [1]}. Similarly to the previous graph classification tasks, we set \(k\)  to mean number of nodes (\(n \approx 26\) ), considered number of steps \(\ell \in \lbrace 8, 16\rbrace \) , batch size \(\in \lbrace 32, 64\rbrace \)  and hidden units \(\in \lbrace 64, 128\rbrace \) . We train the model with 10 random seeds for 100 epochs, select the model with the best validation ROC-AUC and report the mean and the standard deviation. The best setup proved to be using a batch size of 64, 128 hidden units, and \(\ell = 16\)  steps. In general, over most of the tasks we tested, we observed that a larger batch size tends to improve the AgentNet training and that having around 128 hidden units is a good choice, at least with our considered range of values for the number of agents \(k\) .
